suppressPackageStartupMessages({
  library(tidyverse)
  library(MOFA2)
  library(Matrix)
  library(SingleCellExperiment)
  library(scran)
  library(glue)
  library(scater)
  library(patchwork)
  library(batchelor)
  library(rhdf5)
  library(ggraph)
  }
  )

Define plotting utils

remove_x_axis <- function(){
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())  
}

remove_y_axis <- function(){
  theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank())  
}

org_colors <- read_csv("~/Pan_fetal_immune/metadata/organ_colors.csv")
Missing column names filled in: 'X1' [1]Error in (function (srcref)  : unimplemented type (29) in 'eval'
figdir <- "~/mount/gdrive/Pan_fetal/Updates_and_presentations/figures/MOFA_analysis/"
if (!dir.exists(figdir)){ dir.create(figdir) }

Load pseudobulked data

indir <- glue("/nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_{split}_PBULK/")
Error: glue cannot interpolate functions into strings.
* object 'split' is a function.
## Plot number of cells per organ/celltype pair
n_cells_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_cells=sum(n_cells)) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=log10(n_cells))) +
  geom_text(aes(label=n_cells), color="white") +
  scale_fill_viridis_c() +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())
`summarise()` has grouped output by 'anno_lvl_2_final_clean'. You can override using the `.groups` argument.
n_samples_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_samples=n()) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=n_samples)) +
  geom_text(aes(label=n_samples), color="white") +
  scale_fill_viridis_c(option="cividis") +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
`summarise()` has grouped output by 'anno_lvl_2_final_clean'. You can override using the `.groups` argument.
n_cells_heatmap / n_samples_heatmap

Preprocessing

Filtering samples

# Exclude celltypes present in just one organ
keep_ct <- data.frame(colData(sce)) %>%
  select(organ, anno_lvl_2_final_clean) %>%
  distinct() %>%
  group_by(anno_lvl_2_final_clean) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  filter(n > 1) %>%
  pull(anno_lvl_2_final_clean)
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function 'select' for signature '"data.frame"'
## Plot number of cells per organ/celltype pair
n_cells_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_cells=sum(n_cells)) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=log10(n_cells))) +
  geom_text(aes(label=n_cells), color="white") +
  scale_fill_viridis_c() +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())
`summarise()` has grouped output by 'anno_lvl_2_final_clean'. You can override using the `.groups` argument.
n_samples_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_samples=n()) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=n_samples)) +
  geom_text(aes(label=n_samples), color="white") +
  scale_fill_viridis_c(option="cividis") +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))
`summarise()` has grouped output by 'anno_lvl_2_final_clean'. You can override using the `.groups` argument.
n_cells_heatmap / n_samples_heatmap

Technical effect correction

## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(sce), sce$anno_lvl_2_final_clean)
all_hvgs <- c()
for (i in anno_groups){
  dec <- modelGeneVar(sce[,i])
  hvgs <- getTopHVGs(dec, n = 1000)
  all_hvgs <- union(all_hvgs, hvgs)
  }

sce <- sce[which(rowSums(logcounts(sce)) > 0),]
sce
class: SingleCellExperiment 
dim: 28260 993 
metadata(0):
assays(1): logcounts
rownames(28260): TSPAN6 TNMD ... AP000646.1 AP006216.3
rowData names(0):
colnames(993): F45_SK_CD45P_FCAImmP7579224-F45-SK-CD4+T-12-5GEX F45_SK_CD45P_FCAImmP7579224-F45-SK-CD8+T-12-5GEX ...
  F50_SP_CD45P_FCAImmP7803020-F50-SP-IMMATURE_B-15-5GEX F30_TH_CD45N_FCAImmP7277565-F30-TH-ABT(ENTRY)-14-3GEX
colData names(7): Sample donor ... method n_cells
reducedDimNames(0):
altExpNames(0):

EDA with PCA

sce <- runPCA(sce, scale=TRUE, ncomponents=30, 
              exprs_values = "logcounts", subset_row=all_hvgs)

# ## Variance explained
# percent.var <- attr(reducedDim(sce), "percentVar")
# plot(percent.var, log="y", xlab="PC", ylab="Variance explained (%)")
plotPCA(sce, colour_by="donor", ncomponents=6)

plotPCA(sce, colour_by="method", ncomponents=6)

plotPCA(sce, colour_by="organ", ncomponents=10)

Minimize obvious technical effects (3GEX/5GEX) using linear regression (following procedure from OSCA)

## Regress technical effects
design <- model.matrix(~donor+method,data=colData(sce))
residuals <- regressBatches(sce, assay.type = "logcounts", design = design)
assay(sce, "corrected_logcounts") <- as.matrix(assay(residuals[,colnames(sce)], "corrected"))

## Regress organ (soup effect)
design <- model.matrix(~organ,data=colData(sce)) ## Include organ term to capture soup
residuals <- regressBatches(sce, assay.type = "corrected_logcounts", design = design)
assay(sce, "corrected_logcounts") <- as.matrix(assay(residuals[,colnames(sce)], "corrected"))

Check regression has an effect repeating PCA

sce <- runPCA(sce, scale=TRUE, ncomponents=30, exprs_values = "corrected_logcounts")

plotPCA(sce, colour_by="method", ncomponents=6)

plotPCA(sce, colour_by="donor", ncomponents=6)

plotPCA(sce, colour_by="organ", ncomponents=8)

Feature selection

## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(sce), sce$anno_lvl_2_final_clean)
all_hvgs <- c()
for (i in anno_groups){
  dec <- modelGeneVar(sce[,i], assay.type = "corrected_logcounts")
  hvgs <- getTopHVGs(dec, n = 1000)
  all_hvgs <- union(all_hvgs, hvgs)
  }

FA Model - Normal MOFA / only celltypes as groups

Make MOFA object (Use celltypes as grouping covariate)

mofa_obj <- readRDS(glue('{indir}LYMPHOID_mofa_obj_organCorrected.RDS'))
Loading required package: MOFA2

Attaching package: ‘MOFA2’

The following object is masked from ‘package:stats’:

    predict
object <- mofa_obj

Prepare 4 training

object
Untrained MOFA model with the following characteristics: 
 Number of views: 1 
 Views names: corrected_logcounts 
 Number of features (per view): 7300 
 Number of groups: 23 
 Groups names: ABT(ENTRY) B1 CD4+T CD8+T CD8AA CYCLING_MPP CYCLING_NK CYCLING_T HSC_MPP ILC3 IMMATURE_B LARGE_PRE_B LATE_PRO_B LMPP_ELP MATURE_B MEMP NK NK_T PRE_PRO_B PRO_B SMALL_PRE_B TH17 TREG 
 Number of samples (per group): 26 32 63 55 24 28 61 38 32 62 29 54 32 10 55 26 87 52 40 50 46 43 48 

Train

Wrapped in run_mofa.R

# install.packages(renv)
# renv::init()
renv::install("reticulate")
renv::use_python()

py_pkgs <- c(
    "scanpy",
    "anndata",
    "mofapy2"
)

reticulate::py_install(py_pkgs)
mofa_trained <- run_mofa(object, outfile = outfile)
Warning: Output file /nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_LYMPHOID_PBULK/LYMPHOID_mofa_model_oneview_organCorrected.hdf5 already exists, it will be replaced
Connecting to the mofapy2 python package using reticulate (use_basilisk = FALSE)... 
    Please make sure to manually specify the right python binary when loading R with reticulate::use_python(..., force=TRUE) or the right conda environment with reticulate::use_condaenv(..., force=TRUE)
    If you prefer to let us automatically install a conda environment with 'mofapy2' installed using the 'basilisk' package, please use the argument 'use_basilisk = TRUE'

        #########################################################
        ###           __  __  ____  ______                    ### 
        ###          |  \/  |/ __ \|  ____/\    _             ### 
        ###          | \  / | |  | | |__ /  \ _| |_           ### 
        ###          | |\/| | |  | |  __/ /\ \_   _|          ###
        ###          | |  | | |__| | | / ____ \|_|            ###
        ###          |_|  |_|\____/|_|/_/    \_\              ###
        ###                                                   ### 
        ######################################################### 
       
 
        
Successfully loaded view='corrected_logcounts' group='ABT(ENTRY)' with N=26 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='B1' with N=32 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CD4+T' with N=63 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CD8+T' with N=55 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CD8AA' with N=24 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CYCLING_MPP' with N=28 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CYCLING_NK' with N=61 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='CYCLING_T' with N=38 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='HSC_MPP' with N=32 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='ILC3' with N=62 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='IMMATURE_B' with N=29 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='LARGE_PRE_B' with N=54 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='LATE_PRO_B' with N=32 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='LMPP_ELP' with N=10 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='MATURE_B' with N=55 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='MEMP' with N=26 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='NK' with N=87 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='NK_T' with N=52 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='PRE_PRO_B' with N=40 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='PRO_B' with N=50 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='SMALL_PRE_B' with N=46 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='TH17' with N=43 samples and D=7300 features...
Successfully loaded view='corrected_logcounts' group='TREG' with N=48 samples and D=7300 features...


WARNING: 'ard_factors' in model_options should be set to True if using multiple groups unless you are using MEFISTO

Model options:
- Automatic Relevance Determination prior on the factors: False
- Automatic Relevance Determination prior on the weights: True
- Spike-and-slab prior on the factors: False
- Spike-and-slab prior on the weights: True
Likelihoods:
- View 0 (corrected_logcounts): gaussian



Warning: some group(s) have less than 15 samples, MOFA won't be able to learn meaningful factors for these group(s)...



######################################
## Training the model with seed 2020 ##
######################################


ELBO before training: -128754892.30 

Iteration 1: time=12.72, ELBO=1546417.23, deltaELBO=130301309.528 (101.20105512%), Factors=30
Iteration 2: time=12.40, ELBO=2808114.37, deltaELBO=1261697.141 (0.97992171%), Factors=30
Iteration 3: time=12.55, ELBO=3165428.61, deltaELBO=357314.241 (0.27751508%), Factors=30
Iteration 4: time=12.64, ELBO=3248002.54, deltaELBO=82573.933 (0.06413266%), Factors=30
Iteration 5: time=12.77, ELBO=3275153.02, deltaELBO=27150.480 (0.02108695%), Factors=30
Iteration 6: time=12.74, ELBO=3292546.06, deltaELBO=17393.044 (0.01350865%), Factors=30
Iteration 7: time=12.60, ELBO=3304528.49, deltaELBO=11982.426 (0.00930639%), Factors=30
Iteration 8: time=12.40, ELBO=3314496.32, deltaELBO=9967.833 (0.00774171%), Factors=30
Iteration 9: time=12.42, ELBO=3323682.59, deltaELBO=9186.263 (0.00713469%), Factors=30
Iteration 10: time=12.42, ELBO=3332226.53, deltaELBO=8543.946 (0.00663582%), Factors=30
Iteration 11: time=12.37, ELBO=3339946.37, deltaELBO=7719.842 (0.00599577%), Factors=30
Iteration 12: time=12.34, ELBO=3346980.33, deltaELBO=7033.956 (0.00546306%), Factors=30
Iteration 13: time=12.38, ELBO=3353713.28, deltaELBO=6732.948 (0.00522928%), Factors=30
Iteration 14: time=12.39, ELBO=3360238.09, deltaELBO=6524.816 (0.00506763%), Factors=30
Iteration 15: time=12.44, ELBO=3366188.23, deltaELBO=5950.139 (0.00462129%), Factors=30
Iteration 16: time=12.51, ELBO=3371048.61, deltaELBO=4860.380 (0.00377491%), Factors=30
Iteration 17: time=12.89, ELBO=3374570.98, deltaELBO=3522.370 (0.00273572%), Factors=30
Iteration 18: time=12.55, ELBO=3376839.12, deltaELBO=2268.136 (0.00176159%), Factors=30
Iteration 19: time=12.73, ELBO=3378192.86, deltaELBO=1353.743 (0.00105141%), Factors=30
Iteration 20: time=12.52, ELBO=3378991.95, deltaELBO=799.091 (0.00062063%), Factors=30
Iteration 21: time=13.01, ELBO=3379480.81, deltaELBO=488.855 (0.00037968%), Factors=30
Iteration 22: time=13.31, ELBO=3379799.31, deltaELBO=318.506 (0.00024737%), Factors=30
Iteration 23: time=12.88, ELBO=3380022.73, deltaELBO=223.413 (0.00017352%), Factors=30
Iteration 24: time=12.63, ELBO=3380190.96, deltaELBO=168.236 (0.00013066%), Factors=30
Iteration 25: time=12.34, ELBO=3380325.28, deltaELBO=134.315 (0.00010432%), Factors=30
Iteration 26: time=12.58, ELBO=3380438.01, deltaELBO=112.735 (0.00008756%), Factors=30
Iteration 27: time=12.79, ELBO=3380536.80, deltaELBO=98.786 (0.00007672%), Factors=30
Iteration 28: time=12.90, ELBO=3380626.07, deltaELBO=89.271 (0.00006933%), Factors=30
Iteration 29: time=12.76, ELBO=3380708.35, deltaELBO=82.280 (0.00006390%), Factors=30
Iteration 30: time=11.58, ELBO=3380785.55, deltaELBO=77.198 (0.00005996%), Factors=30
Iteration 31: time=11.44, ELBO=3380859.00, deltaELBO=73.448 (0.00005704%), Factors=30
Iteration 32: time=11.31, ELBO=3380929.41, deltaELBO=70.411 (0.00005469%), Factors=30
Iteration 33: time=11.48, ELBO=3380997.29, deltaELBO=67.881 (0.00005272%), Factors=30
Iteration 34: time=11.42, ELBO=3381063.10, deltaELBO=65.813 (0.00005112%), Factors=30
Iteration 35: time=11.60, ELBO=3381127.15, deltaELBO=64.045 (0.00004974%), Factors=30
Iteration 36: time=12.96, ELBO=3381189.61, deltaELBO=62.460 (0.00004851%), Factors=30

Converged!



#######################
## Training finished ##
#######################


Warning: Output file /nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_LYMPHOID_PBULK/LYMPHOID_mofa_model_oneview_organCorrected.hdf5 already exists, it will be replaced
Saving model in /nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_LYMPHOID_PBULK/LYMPHOID_mofa_model_oneview_organCorrected.hdf5...
23 factors were found to explain little or no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = F)
Error in .quality_control(object, verbose = verbose) : 
  !duplicated(unlist(samples_names(object))) are not all TRUE
  # Remove inactive factors
  if (remove_inactive_factors) {
    r2 <- rowSums(do.call('cbind', lapply(object@cache[["variance_explained"]]$r2_per_factor, rowSums, na.rm=TRUE)))
    var.threshold <- 0.0001
    if (all(r2 < var.threshold)) {
      warning(sprintf("All %s factors were found to explain little or no variance so remove_inactive_factors option has been disabled.", length(r2)))
    } else if (any(r2 < var.threshold)) {
      object <- subset_factors(object, which(r2>=var.threshold), recalculate_variance_explained=FALSE)
      message(sprintf("%s factors were found to explain no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = FALSE)", sum(r2 < var.threshold)))
    }
  }
Error in subset_factors(object, which(r2 >= var.threshold), recalculate_variance_explained = FALSE) : 
  unused argument (recalculate_variance_explained = FALSE)

Visualize variance explained by factors

plot_variance_explained(mofa_trained, x='factor', y='group', split_by = 'view', plot_total = TRUE, max_r2 = 50)[[1]] +
  theme(axis.text.x = element_text(angle=45, hjust=1))

get_variance_explained(mofa_trained, as.data.frame = TRUE)[[2]] %>%
  ggplot(aes(group, value)) +
  geom_col() +
  coord_flip() +
  ylab("Var. (%)") +
  theme_classic(base_size=14)

Plot by celltype

get_variance_explained(mofa_trained, as.data.frame = TRUE)[[1]] %>%
  ggplot(aes(factor, value)) + geom_col() +
  coord_flip() +
  facet_wrap(group~., ncol = 6, scales = "free_x")
plot_factor_cor(mofa_trained, method = "spearman")
## Correlation with principal components
pcs <- reducedDim(sce)
fctrs <- get_factors(mofa_trained) %>%
  purrr::reduce(rbind)

corrplot::corrplot(cor(pcs, fctrs[rownames(pcs),]))

Factor ID plots

plot_factor_ordered <- function(mofa_trained, f){
  factor_df <- get_factors(mofa_trained, factors = f, as.data.frame = TRUE) %>%
      mutate(organ = sapply(str_split(sample, "_"), function(x) x[2])) %>%
      group_by(group) %>%
      mutate(gr_mean = median(value)) %>%
      ungroup() %>%
      arrange(gr_mean) %>%
      mutate(group=factor(group, levels=unique(group))) 
  
  r2_df <- get_variance_explained(mofa_trained, factors = f, as.data.frame = TRUE)[[1]] %>%
    filter(factor==paste0('Factor',f)) %>%
    mutate(group=factor(group, levels = levels(factor_df$group)))
  
  pl1 <- factor_df %>%
      ggplot(aes(group, value)) +
      geom_boxplot() +
      geom_jitter(aes(color= organ), size=0.7) +
      geom_hline(yintercept = 0, linetype=2) +
      coord_flip() +
      ylab(paste0("Factor ", f)) +
      theme_bw(base_size = 14)
  
  pl2 <- r2_df %>%
    ggplot(aes(group, value)) +
    geom_col() +
    coord_flip() +
    ylab("% variance explained") +
    theme_bw(base_size = 14) +
    remove_y_axis()
  
  pl1 + pl2 + plot_layout(widths=c(2,1), guides="collect") 
}

get_top_celltype_per_factor <- function(mofa_trained, f){
  r2_df <- get_variance_explained(mofa_trained, factors = f, as.data.frame = TRUE)[[1]] %>%
    filter(factor==paste0('Factor',f)) 
    # mutate(group=factor(group, levels = ))
  top_quant_r2 <- quantile(r2_df$value, probs = seq(0, 1, by = 0.2))["80%"]
  top_groups <- r2_df$group[r2_df$value >= top_quant_r2]
  return(top_groups)
}

save_factor_id <- function(mofa_trained, f, figdir){
  ## Order celltypes by factor values
  p1 <- plot_factor_ordered(mofa_trained, f)
  
  ## Plot factor values across organs for celltypes with high variance explained
  p2 <- plot_factor(mofa_trained, factors = f, groups = get_top_celltype_per_factor(mofa_trained, f), group_by = "group", 
              color_by = "organ", 
              dot_size = 2, dodge = TRUE
              )
  
  ## Plot factor weights on genes
  # plot_data_heatmap(mofa_trained, factor = f, nfeatures = 50, text_size = 3, show_colnames=FALSE,
  #                   annotation_samples = c("organ", "time", "method", "donor"))
  p3 <- plot_weights(mofa_trained, factors = f, nfeatures = 30, text_size = 3) +
   scale_y_discrete(expand=c(0.1, 0.1))
  
  (p1 | (p2 / p3)) +
    plot_layout(guides="collect") +
    ggsave(glue("{figdir}/MOFA_{split}_factorID_factor{f}.pdf"), width = 15, height = 10)
}

for (f in 1:mofa_trained@dimensions$K){
  print(paste0("Saving ID for Factor ", f, "..."))
  save_factor_id(mofa_trained, f=f, figdir = figdir)  
}

# save_factor_id(mofa_trained, f=1, figdir = figdir)  
# plot_weights(mofa_trained, factors = f, nfeatures = 30, text_size = 3) +
#    scale_y_discrete(expand=c(0.1, 0.1))

KNN graph per celltype

## Get factors that explain most variance in each celltype
get_top_factor_per_celltype <- function(mofa_trained, gr, min_R2=2){
  get_variance_explained(mofa_trained, as.data.frame = TRUE)[[1]] %>%
    filter(group==gr) %>%
    filter(value >= min_R2) %>%
    pull(factor) %>%
    as.character()
}

## Make KNN graph based on similarity of top factors for each celltype
get_ct_KNN_graph <- function(mofa_trained, gr, min_R2=5, k=5){
  ## Get factors that explain most variance per celltype
  fs <- get_top_factor_per_celltype(mofa_trained, gr, min_R2 = min_R2)
  
  ## Make KNN graph from top factors
  Z <- get_factors(mofa_trained, groups=gr, factors = fs)[[1]]
  knn_ct <- buildKNNGraph(t(Z), k=k)
  
  ## Add attributes
  metadata_ct <- samples_metadata(mofa_trained)[rownames(Z),]
  # covariates
  V(knn_ct)$organ <- metadata_ct$organ
  V(knn_ct)$age <- metadata_ct$age
  V(knn_ct)$n_cells <- metadata_ct$n_cells
  V(knn_ct)$method <- metadata_ct$method
  V(knn_ct)$donor <- metadata_ct$donor
  # top factors
  for (c in colnames(Z)){
   vertex_attr(knn_ct)[[c]] <- Z[,c]  
  }
  
  return(knn_ct)
  }

## Plot KNN graph
plot_ct_KNN_graph <- function(knn, color_by="organ"){
  ## Define color 
  if (!color_by %in% names(vertex_attr(knn))){
    stop("specified color_by variable is not in vertex_attr(knn)")
  }
  
  if (color_by=="organ"){ 
    scale_color_knngraph <- scale_color_manual(values=org_colors)
  } else if (is.numeric(vertex_attr(knn, color_by))){
    scale_color_knngraph <- scale_color_viridis_c(option="magma")  
  } else {
      scale_color_knngraph <- scale_color_discrete()
    }
  
  vertex_attr(knn, "color_by") <- vertex_attr(knn, color_by)
  
  ggraph(knn) +
    geom_edge_link0() +
    geom_node_point(aes(color=color_by, size=n_cells)) +
    theme(panel.background = element_blank()) +
    scale_color_knngraph +
    scale_size(range=c(2,7)) 
  }

get_top_factor_per_celltype(mofa_trained, "CD8+T", min_R2 = 5)
plot_ct_KNN_graph(get_ct_KNN_graph(mofa_trained, "SMALL PRE B CELL", k=5), color_by = 'organ') +
  plot_ct_KNN_graph(get_ct_KNN_graph(mofa_trained, "SMALL PRE B CELL", k=5), color_by = 'Factor4')

all_groups <- names(get_data(mofa_trained)[[1]])
knn_graph_pl <- lapply(all_groups, function(g){
  knn <- get_ct_KNN_graph(mofa_trained, g, k=5, min_R2 = 2)
  plot_ct_KNN_graph(knn, color_by = 'organ') + ggtitle(g)
  })

knn_graph_pl <- setNames(knn_graph_pl, all_groups)
knn_graph_pl$TREG
## Score connectivity between samples from the same organ
.calc_connectivity_score <- function(knn, o){
  adj <- get.adjacency(knn)
  n_org <- sum(V(knn)$organ==o)
  n_other <- sum(V(knn)$organ!=o)
  within_edges <- sum(adj[V(knn)$organ==o,V(knn)$organ==o])
  between_edges <- sum(adj[V(knn)$organ==o,V(knn)$organ!=o])
  score <- (within_edges/between_edges)*(n_other/n_org)
  return(score)
  }

## Calculate connectivity score for permutations of node labels
conn_score_test <- function(knn, o, n_perm=1000){
  real_score <- .calc_connectivity_score(knn, o)
  ## Random permutations
  rand_scores <- c()
  for (i in 1:n_perm){
    rand_knn <- knn
    V(rand_knn)$organ <- sample(V(knn)$organ)
    rand_scores <- c(rand_scores, .calc_connectivity_score(rand_knn, o))   
  }
  
  p_val <- sum(c(rand_scores, real_score) >= real_score)/(n_perm + 1)
  if (p_val < 2e-16){ p_val <- 2e-16}
  return(c('score'=real_score,'p_value'=p_val))
}

## Calculate connectivity score + significance with permutation test
test_conn_group <- function(mofa_trained, g, k=5, min_R2 = 2, n_perm=1000){
  knn <- get_ct_KNN_graph(mofa_trained, g, k=k, min_R2 = min_R2)
  test_orgs <- names(table(V(knn)$organ))[table(V(knn)$organ) > 2]
  return(sapply(test_orgs, function(o) conn_score_test(knn, o, n_perm=n_perm)))
  }

connectivity_test_ls <- lapply(all_groups, function(g) test_conn_group(mofa_trained, g))
connectivity_test_ls <- setNames(connectivity_test_ls, all_groups)

connectivity_test_df <- imap(connectivity_test_ls, ~ data.frame(t(.x)) %>% rownames_to_column("organ") %>% mutate(group=.y)) %>%
  purrr::reduce(bind_rows) %>%
  mutate(is_signif = ifelse(p_value < 0.01, TRUE, FALSE)) 

connectivity_test_df %>%
  ggplot(aes(organ, group,fill=log10(score))) +
  geom_tile() +
  scale_fill_distiller(palette="Reds", direction = 1) +
  geom_text(data=. %>% filter(is_signif), label="*", size=5)
connectivity_test_df %>%
  group_by(group) %>%
  mutate(mean_val=median(score)) %>%
  ungroup() %>%
  arrange(-mean_val) %>%
  mutate(group=factor(group, levels=unique(group))) %>%
  ggplot(aes(organ, log1p(score))) +
  geom_col(fill="grey") +
  geom_col(data=. %>% filter(is_signif), aes(fill=organ)) +
  scale_fill_manual(values=org_colors)  +
  coord_flip() +
  facet_grid(group~.) +
  theme(strip.text.y = element_text(angle=0))

Expression of top R2 factors

get_top_weight_genes <- function(mofa_trained, f, n_top=20, which="top"){
  w_df <- get_weights(mofa_trained, factors = f, as.data.frame = TRUE) %>%
    arrange(value) 
  if (which=="top") {
    w_df %>%
      top_n(n_top, value) %>%
      pull(feature) %>%
      as.character()
  } else if (which=="bottom"){
    w_df %>%
      top_n(n_top, -value) %>%
      pull(feature) %>%
      as.character()
    }
}

plot_data_top_weights <- function(mofa_trained, ct, f, n_top=20, which="top"){
  genes <- get_top_weight_genes(mofa_trained, f, which=which, n_top=n_top)
  data <- get_data(mofa_trained, groups=ct)[[1]][[1]][genes,]
  
  pl_df <- reshape2::melt(data, varnames=c("gene", "sample")) %>%
    left_join(samples_metadata(mofa_trained)) %>%
    arrange(age) %>%
    mutate(sample=factor(sample, levels=unique(sample))) %>%
    group_by(gene) %>%
    mutate(value=scale(value))
  pl_df %>%
    ggplot(aes(sample, gene, fill=value)) +
    geom_tile() +
    facet_grid(.~organ, space="free", scales="free") +
    scale_fill_gradient2(high="red", low="blue", name="Scaled\nexpression") +
    xlab("----age--->") + ylab(glue("{which} weight genes")) +
    theme_bw(base_size=16) +
    theme(axis.ticks.x = element_blank(), axis.text.x = element_blank()) +
    ggtitle(glue('{ct} - {f}'))
}

for (g in all_groups){
  fs <- get_top_factor_per_celltype(mofa_trained, g, min_R2=3)
  top_plots <- lapply(fs, function(x) (plot_data_top_weights(mofa_trained, g, x, which="top") + remove_x_axis()) /  
                        plot_data_top_weights(mofa_trained, g, x, which="bottom") + ggtitle("")
  )
  wrap_plots(top_plots, ncol=1) +
  ggsave(glue("{figdir}/top_factors_expr_{g}.pdf"), width=8, height = 7*length(top_plots))
}
plot_data_heatmap(mofa_trained, factor = 2, show_colnames=FALSE, annotation_samples = c("anno_lvl_2_final_clean", "organ"))
plot_factor(mofa_trained, factor=25, color_by="method", dot_size = 4)

GSEA

# BiocManager::install("MOFAdata")
library(MOFAdata)
utils::data(reactomeGS)
head(rownames(reactomeGS))

## Remove row with NA
reactomeGS <- reactomeGS[!is.na(rownames(reactomeGS)),]
library(EnsDb.Hsapiens.v86)
hg.pairs <- readRDS(system.file("exdata", "human_cycle_markers.rds", package="scran"))
all_genes <- ensembldb::genes(EnsDb.Hsapiens.v86)
detach(package:EnsDb.Hsapiens.v86)
detach(package:ensembldb)

# gene_name_2_id <- function(gene){
#    return(all_genes[all_genes$gene_name==gene,]$gene_id[1])
# }
# 
# gene_ids <- sapply(mofa_trained@features_metadata$feature, gene_name_2_id)
# rowData(sce)["gene_id"] <- gene_ids
# rowData(sce)["gene_name"] <- rownames(sce)

gene_names_reactome <- all_genes[colnames(reactomeGS)]$gene_name
colnames(reactomeGS) <- gene_names_reactome

Subset to genes tested

reactomeGS_universe <- reactomeGS[, colnames(reactomeGS) %in% mofa_trained@features_metadata$feature]
# GSEA on positive weights, with default options
res.positive <- run_enrichment(mofa_trained,
  view='scaled_logcounts',
  # statistical.test = 'cor.adj.parametric',
  feature.sets = reactomeGS_universe, 
  sign = "positive",
)

# GSEA on negative weights, with default options
res.negative <- run_enrichment(mofa_trained, 
  view='scaled_logcounts',
  # statistical.test = 'cor.adj.parametric',
  feature.sets = reactomeGS_universe, 
  sign = "negative"
)


for (f in 1:mofa_trained@dimensions$K){
  if (min(res.positive$pval.adj[,paste0("Factor", f)]) < 0.1) {
    print(plot_enrichment(res.positive, factor = f, alpha=0.1) + ggtitle("Positive weights") +
            plot_enrichment(res.negative, factor = f, alpha=0.1) + ggtitle("Negative weights") +
              plot_annotation(title=paste0("Factor", f)))
      }
  }
signif_pathways <- rownames(data.frame(res.negative$pval.adj))[order(data.frame(res.negative$pval.adj)[["Factor8"]])[0:10]]
colnames(reactomeGS_universe)[reactomeGS_universe[signif_pathways[5],]==1]
plot_enrichment_detailed(res.negative, factor = 8)

–> –> –> –> –> –> –> –> –> –> –> –> –> –> –> –> –>

–> –> –> –> –>

–> –> –>

---
title: "Factor Analysis for within-celltype differences on on pan-fetal immune"
output: html_notebook
---
```{r}
suppressPackageStartupMessages({
  library(tidyverse)
  library(MOFA2)
  library(Matrix)
  library(SingleCellExperiment)
  library(scran)
  library(glue)
  library(scater)
  library(patchwork)
  library(batchelor)
  library(rhdf5)
  library(ggraph)
  }
  )
```

Define plotting utils
```{r}
remove_x_axis <- function(){
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())  
}

remove_y_axis <- function(){
  theme(axis.text.y = element_blank(), axis.ticks.y = element_blank(), axis.title.y = element_blank())  
}

org_colors <- read_csv("~/Pan_fetal_immune/metadata/organ_colors.csv")
org_colors <- setNames(org_colors$color, org_colors$organ)
```

```{r}
figdir <- "~/mount/gdrive/Pan_fetal/Updates_and_presentations/figures/MOFA_analysis/"
if (!dir.exists(figdir)){ dir.create(figdir) }
```

## Load pseudobulked data

```{r}
split = "LYMPHOID"
indir <- glue("/nfs/team205/ed6/data/Fetal_immune/LMM_data/LMM_input_{split}_PBULK/")

matrix <- readMM(file = paste0(indir, "matrix.mtx.gz"))
coldata <- read.csv(file = paste0(indir, "metadata.csv.gz"))  %>%
  column_to_rownames("X")
rowdata <- read.csv(file = paste0(indir, "gene.csv.gz")) 

## Make SingleCellExperiment obj
sce <- SingleCellExperiment(list(logcounts = t(matrix)), colData = coldata)
rownames(sce) <- make.unique(rowdata$GeneName) 
```

```{r, fig.width=15, fig.height=10}
## Plot number of cells per organ/celltype pair
n_cells_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_cells=sum(n_cells)) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=log10(n_cells))) +
  geom_text(aes(label=n_cells), color="white") +
  scale_fill_viridis_c() +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())

n_samples_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_samples=n()) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=n_samples)) +
  geom_text(aes(label=n_samples), color="white") +
  scale_fill_viridis_c(option="cividis") +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))

n_cells_heatmap / n_samples_heatmap
```

## Preprocessing

### Filtering samples

```{r}
## Filter out samples with less than 20 cells
sce <- sce[,sce$n_cells > 20]

# Exclude celltypes present in just one organ
keep_ct <- data.frame(colData(sce)) %>%
  dplyr::select(organ, anno_lvl_2_final_clean) %>%
  distinct() %>%
  group_by(anno_lvl_2_final_clean) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  filter(n > 1) %>%
  pull(anno_lvl_2_final_clean)

sce <- sce[,sce$anno_lvl_2_final_clean %in% keep_ct]

# Filter out celltypes with less than 10 samples
keep_ct <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean) %>%
  summarise(n_samples=n()) %>%
  filter(n_samples >= 10) %>%
  pull(anno_lvl_2_final_clean)

sce <- sce[,sce$anno_lvl_2_final_clean %in% keep_ct]

## Exclude low quality clusters
library("rjson")
anno_groups <- fromJSON(file = "~/Pan_fetal_immune/metadata/anno_groups.json")
sce <- sce[,!sce$anno_lvl_2_final_clean %in% anno_groups$OTHER]

## Exclude donor F19 (low Q)
sce <- sce[,!sce$donor %in% c('F19')]
```

```{r, fig.width=15, fig.height=10}
## Plot number of cells per organ/celltype pair
n_cells_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_cells=sum(n_cells)) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=log10(n_cells))) +
  geom_text(aes(label=n_cells), color="white") +
  scale_fill_viridis_c() +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank())

n_samples_heatmap <- data.frame(colData(sce)) %>%
  group_by(anno_lvl_2_final_clean, organ) %>%
  summarise(n_samples=n()) %>%
  ggplot(aes(anno_lvl_2_final_clean, organ)) +
  geom_tile(aes(fill=n_samples)) +
  geom_text(aes(label=n_samples), color="white") +
  scale_fill_viridis_c(option="cividis") +
  theme_classic(base_size = 16) +
  xlab("celltype") +
  theme(axis.text.x = element_text(angle=90, hjust=1, vjust=0.5))

n_cells_heatmap / n_samples_heatmap
```

### Technical effect correction 

```{r}
## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(sce), sce$anno_lvl_2_final_clean)
all_hvgs <- c()
for (i in anno_groups){
  dec <- modelGeneVar(sce[,i])
  hvgs <- getTopHVGs(dec, n = 1000)
  all_hvgs <- union(all_hvgs, hvgs)
  }

sce <- sce[which(rowSums(logcounts(sce)) > 0),]
sce
```

EDA with PCA
```{r, fig.height=15, fig.width=15}
sce <- runPCA(sce, scale=TRUE, ncomponents=30, 
              exprs_values = "logcounts", subset_row=all_hvgs)

# ## Variance explained
# percent.var <- attr(reducedDim(sce), "percentVar")
# plot(percent.var, log="y", xlab="PC", ylab="Variance explained (%)")
plotPCA(sce, colour_by="donor", ncomponents=6)
plotPCA(sce, colour_by="method", ncomponents=6)
plotPCA(sce, colour_by="organ", ncomponents=10)
```

Minimize obvious technical effects (3GEX/5GEX) using linear regression (following procedure from [OSCA](https://bioconductor.org/books/release/OSCA/integrating-datasets.html#linear-regression))

```{r}
## Regress technical effects
design <- model.matrix(~donor+method,data=colData(sce))
residuals <- regressBatches(sce, assay.type = "logcounts", design = design)
assay(sce, "corrected_logcounts") <- as.matrix(assay(residuals[,colnames(sce)], "corrected"))

## Regress organ (soup effect)
design <- model.matrix(~organ,data=colData(sce)) ## Include organ term to capture soup
residuals <- regressBatches(sce, assay.type = "corrected_logcounts", design = design)
assay(sce, "corrected_logcounts") <- as.matrix(assay(residuals[,colnames(sce)], "corrected"))

```

Check regression has an effect repeating PCA
```{r, fig.height=15, fig.width=15}
sce <- runPCA(sce, scale=TRUE, ncomponents=30, exprs_values = "corrected_logcounts")

plotPCA(sce, colour_by="method", ncomponents=6)
plotPCA(sce, colour_by="donor", ncomponents=6)
plotPCA(sce, colour_by="organ", ncomponents=8)
```

### Feature selection

```{r}
## Feature selection w scran WITHIN CELLTYPE
anno_groups <- split(colnames(sce), sce$anno_lvl_2_final_clean)
all_hvgs <- c()
for (i in anno_groups){
  dec <- modelGeneVar(sce[,i], assay.type = "corrected_logcounts")
  hvgs <- getTopHVGs(dec, n = 1000)
  all_hvgs <- union(all_hvgs, hvgs)
  }
```

<!-- ```{r} -->
<!-- data.frame(colData(sce)) %>% -->
<!--   group_by(anno_lvl_2_final_clean, organ) %>% -->
<!--   summarise(n_samples=n(), n_cells=sum(n_cells)) %>% -->
<!--   ggplot(aes(n_samples, log10(n_cells))) + -->
<!--   geom_point(size=0.8, alpha=0.6) -->
<!-- ``` -->

<!-- ```{r, fig.width=10, fig.height=10} -->
<!-- p <- data.frame(reducedDim(sce)[,1:2]) %>% -->
<!--   mutate(organ=sce$organ, celltype=sce$anno_lvl_2_final_clean) %>% -->
<!--   mutate(color=ifelse(celltype=="MATURE B CELL", "ELP", NA)) %>% -->
<!--   ggplot(aes(PC1, PC2)) + -->
<!--   geom_point(color="grey") + -->
<!--   geom_point(data=. %>% filter(!is.na(color)), aes(color=organ), size=2) + -->
<!--   geom_rug(data=. %>% filter(!is.na(color)), aes(color=organ), alpha=0.5) -->

<!-- p -->
<!-- ``` -->

<!-- ```{r} -->
<!-- library(RColorBrewer) -->
<!-- org_colors <- setNames(brewer.pal(9, "Set1"), unique(sce$organ)) -->
<!-- ``` -->


<!-- ```{r} -->
<!-- sce_matureB <- sce[,sce$anno_lvl_2_final_clean=="MATURE B CELL"] -->
<!-- assay(sce_matureB, "scaled_logcounts") <- t(scale(t(logcounts(sce_matureB)))) -->


<!-- sce_matureB <- runPCA(sce_matureB, scale=FALSE, ncomponents=30, exprs_values = "scaled_logcounts") -->

<!-- data.frame(reducedDim(sce_matureB)[,2:3]) %>% -->
<!--   mutate(organ=sce_matureB$organ, celltype=sce_matureB$anno_lvl_2_final_clean) %>% -->
<!--   mutate(color=ifelse(organ %in% c("TH","BM"), "ELP", NA)) %>% -->
<!--   ggplot(aes(PC2, PC3)) + -->
<!--   geom_point(color="grey") + -->
<!--   geom_point(data=. %>% filter(!is.na(color)), aes(color=organ), size=2) + -->
<!--   geom_rug(data=. %>% filter(!is.na(color)), aes(color=organ), alpha=0.5) + -->
<!--   scale_color_manual(values=org_colors) + -->
<!--   theme_bw(base_size=16) + -->
<!--   ggtitle("MATURE B CELL") -->
<!-- ``` -->
<!-- ```{r} -->
<!-- sce_matureB <- sce[,sce$anno_lvl_2_final_clean=="NK"] -->
<!-- assay(sce_matureB, "scaled_logcounts") <- t(scale(t(logcounts(sce_matureB)))) -->

<!-- sce_matureB <- runPCA(sce_matureB, scale=FALSE, ncomponents=30, exprs_values = "scaled_logcounts") -->

<!-- ## Variance explained -->
<!-- data.frame(reducedDim(sce_matureB)[,1:4]) %>% -->
<!--   mutate(organ=sce_matureB$organ,  -->
<!--          celltype=sce_matureB$anno_lvl_2_final_clean) %>% -->
<!--   mutate(color=ifelse(organ %in% c("GU", "SP"), "ELP", NA)) %>% -->
<!--   ggplot(aes(PC1, PC3)) + -->
<!--   geom_point(color="grey") + -->
<!--   geom_point(data=. %>% filter(!is.na(color)), aes(color=organ), size=2) + -->
<!--   geom_rug(data=. %>% filter(!is.na(color)), aes(color=organ), alpha=0.5) + -->
<!--   scale_color_manual(values=org_colors) + -->
<!--   theme_bw(base_size=16) + -->
<!--   ggtitle("NK CELL") -->
<!-- ``` -->


<!-- ```{r} -->
<!-- data.frame(reducedDim(sce_matureB)[,2:3]) %>% -->
<!--   mutate(organ=sce_matureB$organ, celltype=sce_matureB$anno_lvl_2_final_clean) %>% -->
<!--   mutate(color=ifelse(celltype=="MATURE B CELL", "ELP", NA)) %>% -->
<!--   ggplot(aes(PC2, PC3)) + -->
<!--   geom_point(color="grey") + -->
<!--   geom_point(data=. %>% filter(!is.na(color)), aes(color=organ), size=2) + -->
<!--   geom_rug(data=. %>% filter(!is.na(color)), aes(color=organ), alpha=0.5) + -->
<!--   scale_color_brewer(palette="Spectral") -->
<!-- ``` -->


# FA Model - Normal MOFA / only celltypes as groups

Make MOFA object (Use celltypes as grouping covariate)

```{r}
mofa <- create_mofa_from_SingleCellExperiment(sce[all_hvgs,], assay = "corrected_logcounts", 
                                              groups = "anno_lvl_2_final_clean", extract_metadata = TRUE)



saveRDS(mofa, glue('{indir}LYMPHOID_mofa_obj_organCorrected.RDS'))
mofa_obj <- readRDS(glue('{indir}LYMPHOID_mofa_obj_organCorrected.RDS'))
```
```{r}
object <- mofa_obj
```


Prepare 4 training

```{r}

data_opts <- get_default_data_options(object)
data_opts$use_float32 <- TRUE
data_opts$center_groups <- FALSE
object@data_options <- data_opts

model_opts <- get_default_model_options(object)
model_opts$num_factors <- 30
model_opts$ard_factors <- FALSE

train_opts <- get_default_training_options(object)
train_opts$seed <- 2020
train_opts$convergence_mode <- "medium" # use "fast" for faster training
train_opts$stochastic <- FALSE

# mefisto_opts <- get_default_mefisto_options(object)
# mefisto_opts$warping <- FALSE
# mefisto_opts$sparseGP <- TRUE

object <- prepare_mofa(
  object = object,
  data_options = data_opts,
  model_options = model_opts,
  training_options = train_opts
) 

object
```

## Train

Wrapped in `run_mofa.R`

```{r}
# install.packages(renv)
# renv::init()
renv::install("reticulate")
renv::use_python()

py_pkgs <- c(
    "scanpy",
    "anndata",
    "mofapy2"
)

reticulate::py_install(py_pkgs)
```


```{r}
outfile <- glue('{indir}{split}_mofa_model_oneview_organCorrected.hdf5')
mofa_trained <- run_mofa(object, outfile = outfile)
```

```{r}
### Tweaking the MOFA2 loading function because the quality control complains
load_model <- function(file, sort_factors = TRUE, on_disk = FALSE, load_data = TRUE,
                       remove_outliers = FALSE, remove_inactive_factors = TRUE, verbose = FALSE,
                       load_interpol_Z = FALSE) {

  # Create new MOFAodel object
  object <- new("MOFA")
  object@status <- "trained"
  
  # Set on_disk option
  if (on_disk) { 
    object@on_disk <- TRUE 
  } else { 
      object@on_disk <- FALSE 
  }
  
  # Get groups and data set names from the hdf5 file object
  h5ls.out <- h5ls(file, datasetinfo = FALSE)
  
  ########################
  ## Load training data ##
  ########################

  # Load names
  if ("views" %in% h5ls.out$name) {
    view_names <- as.character( h5read(file, "views")[[1]] )
    group_names <- as.character( h5read(file, "groups")[[1]] )
    feature_names <- h5read(file, "features")[view_names]
    sample_names  <- h5read(file, "samples")[group_names] 
  } else {  # for old models
    feature_names <- h5read(file, "features")
    sample_names  <- h5read(file, "samples")
    view_names <- names(feature_names)
    group_names <- names(sample_names)
    h5ls.out <- h5ls.out[grep("variance_explained", h5ls.out$name, invert = TRUE),]
  }
  if("covariates" %in%  h5ls.out$name){
    covariate_names <- as.character( h5read(file, "covariates")[[1]])
  } else {
    covariate_names <- NULL
  }

  # Load training data (as nested list of matrices)
  data <- list(); intercepts <- list()
  if (load_data && "data"%in%h5ls.out$name) {
    
    object@data_options[["loaded"]] <- TRUE
    if (verbose) message("Loading data...")
    
    for (m in view_names) {
      data[[m]] <- list()
      intercepts[[m]] <- list()
      for (g in group_names) {
        if (on_disk) {
          # as DelayedArrays
          data[[m]][[g]] <- DelayedArray::DelayedArray( HDF5ArraySeed(file, name = sprintf("data/%s/%s", m, g) ) )
        } else {
          # as matrices
          data[[m]][[g]] <- h5read(file, sprintf("data/%s/%s", m, g) )
          tryCatch(intercepts[[m]][[g]] <- as.numeric( h5read(file, sprintf("intercepts/%s/%s", m, g) ) ), error = function(e) { NULL })
        }
        # Replace NaN by NA
        data[[m]][[g]][is.nan(data[[m]][[g]])] <- NA # this realised into memory, TO FIX
      }
    }
    
  # Create empty training data (as nested list of empty matrices, with the correct dimensions)
  } else {
    
    object@data_options[["loaded"]] <- FALSE
    
    for (m in view_names) {
      data[[m]] <- list()
      for (g in group_names) {
        data[[m]][[g]] <- .create_matrix_placeholder(rownames = feature_names[[m]], colnames = sample_names[[g]])
      }
    }
  }

  object@data <- data
  object@intercepts <- intercepts


  # Load metadata if any
  if ("samples_metadata" %in% h5ls.out$name) {
    object@samples_metadata <- bind_rows(lapply(group_names, function(g) as.data.frame(h5read(file, sprintf("samples_metadata/%s", g)))))
  }
  if ("features_metadata" %in% h5ls.out$name) {
    object@features_metadata <- bind_rows(lapply(view_names, function(m) as.data.frame(h5read(file, sprintf("features_metadata/%s", m)))))
  }
  
  # ############################
  # ## Load sample covariates ##
  # ############################
  # 
  # if (any(grepl("cov_samples", h5ls.out$group))){
  #   covariates <- list()
  #   for (g in group_names) {
  #     if (on_disk) {
  #       # as DelayedArrays
  #       covariates[[g]] <- DelayedArray::DelayedArray( HDF5ArraySeed(file, name = sprintf("cov_samples/%s", g) ) )
  #     } else {
  #       # as matrices
  #       covariates[[g]] <- h5read(file, sprintf("cov_samples/%s", g) )
  #     }    
  #   }
  # } else covariates <- NULL
  # object@covariates <- covariates

  # if (any(grepl("cov_samples_transformed", h5ls.out$group))){
  #   covariates_warped <- list()
  #   for (g in group_names) {
  #     if (on_disk) {
  #       # as DelayedArrays
  #       covariates_warped[[g]] <- DelayedArray::DelayedArray( HDF5ArraySeed(file, name = sprintf("cov_samples_transformed/%s", g) ) )
  #     } else {
  #       # as matrices
  #       covariates_warped[[g]] <- h5read(file, sprintf("cov_samples_transformed/%s", g) )
  #     }    
  #   }
  # } else covariates_warped <- NULL
  # object@covariates_warped <- covariates_warped
  
  # #######################
  # ## Load interpolated factor values ##
  # #######################
  # 
  # interpolated_Z <- list()
  # if (isTRUE(load_interpol_Z)) {
  #   
  #   if (isTRUE(verbose)) message("Loading interpolated factor values...")
  #   
  #   for (g in group_names) {
  #     interpolated_Z[[g]] <- list()
  #     if (on_disk) {
  #       # as DelayedArrays
  #       # interpolated_Z[[g]] <- DelayedArray::DelayedArray( HDF5ArraySeed(file, name = sprintf("Z_predictions/%s", g) ) )
  #     } else {
  #       # as matrices
  #       tryCatch( {
  #         interpolated_Z[[g]][["mean"]] <- h5read(file, sprintf("Z_predictions/%s/mean", g) )
  #       }, error = function(x) { print("Predicitions of Z not found, not loading it...") })
  #       tryCatch( {
  #         interpolated_Z[[g]][["variance"]] <- h5read(file, sprintf("Z_predictions/%s/variance", g) )
  #       }, error = function(x) { print("Variance of predictions of Z not found, not loading it...") })
  #       tryCatch( {
  #         interpolated_Z[[g]][["new_values"]] <- h5read(file, "Z_predictions/new_values")
  #       }, error = function(x) { print("New values of Z not found, not loading it...") })
  #     }
  #   }
  # }
  # object@interpolated_Z <- interpolated_Z
  
  #######################
  ## Load expectations ##
  #######################

  expectations <- list()
  node_names <- h5ls.out[h5ls.out$group=="/expectations","name"]

  if (verbose) message(paste0("Loading expectations for ", length(node_names), " nodes..."))

  if ("AlphaW" %in% node_names)
    expectations[["AlphaW"]] <- h5read(file, "expectations/AlphaW")[view_names]
  if ("AlphaZ" %in% node_names)
    expectations[["AlphaZ"]] <- h5read(file, "expectations/AlphaZ")[group_names]
  if ("Sigma" %in% node_names)
    expectations[["Sigma"]] <- h5read(file, "expectations/Sigma")
  if ("Z" %in% node_names)
    expectations[["Z"]] <- h5read(file, "expectations/Z")[group_names]
  if ("W" %in% node_names)
    expectations[["W"]] <- h5read(file, "expectations/W")[view_names]
  if ("ThetaW" %in% node_names)
    expectations[["ThetaW"]] <- h5read(file, "expectations/ThetaW")[view_names]
  if ("ThetaZ" %in% node_names)
    expectations[["ThetaZ"]] <- h5read(file, "expectations/ThetaZ")[group_names]
  # if ("Tau" %in% node_names)
  #   expectations[["Tau"]] <- h5read(file, "expectations/Tau")
  
  object@expectations <- expectations

  
  ########################
  ## Load model options ##
  ########################

  if (verbose) message("Loading model options...")

  tryCatch( {
    object@model_options <- as.list(h5read(file, 'model_options', read.attributes = TRUE))
  }, error = function(x) { print("Model options not found, not loading it...") })

  # Convert True/False strings to logical values
  for (i in names(object@model_options)) {
    if (object@model_options[i] == "False" || object@model_options[i] == "True") {
      object@model_options[i] <- as.logical(object@model_options[i])
    } else {
      object@model_options[i] <- object@model_options[i]
    }
  }

  ##########################################
  ## Load training options and statistics ##
  ##########################################

  if (verbose) message("Loading training options and statistics...")

  # Load training options
  if (length(object@training_options) == 0) {
    tryCatch( {
      object@training_options <- as.list(h5read(file, 'training_opts', read.attributes = TRUE))
    }, error = function(x) { print("Training opts not found, not loading it...") })
  }

  # Load training statistics
  tryCatch( {
    object@training_stats <- h5read(file, 'training_stats', read.attributes = TRUE)
    object@training_stats <- h5read(file, 'training_stats', read.attributes = TRUE)
  }, error = function(x) { print("Training stats not found, not loading it...") })

  #############################
  ## Load covariates options ##
  #############################
  
  if (any(grepl("cov_samples", h5ls.out$group))) { 
    if (isTRUE(verbose)) message("Loading covariates options...")
    tryCatch( {
      object@mefisto_options <- as.list(h5read(file, 'smooth_opts', read.attributes = TRUE))
    }, error = function(x) { print("Covariates options not found, not loading it...") })
    
    # Convert True/False strings to logical values
    for (i in names(object@mefisto_options)) {
      if (object@mefisto_options[i] == "False" | object@mefisto_options[i] == "True") {
        object@mefisto_options[i] <- as.logical(object@mefisto_options[i])
      } else {
        object@mefisto_options[i] <- object@mefisto_options[i]
      }
    }
    
  }
  
  
    
  #######################################
  ## Load variance explained estimates ##
  #######################################
  
  if ("variance_explained" %in% h5ls.out$name) {
    r2_list <- list(
      r2_total = h5read(file, "variance_explained/r2_total")[group_names],
      r2_per_factor = h5read(file, "variance_explained/r2_per_factor")[group_names]
    )
    object@cache[["variance_explained"]] <- r2_list
  }
  
  # Hack to fix the problems where variance explained values range from 0 to 1 (%)
  if (max(sapply(object@cache$variance_explained$r2_total,max,na.rm=TRUE),na.rm=TRUE)<1) {
    for (m in 1:length(view_names)) {
      for (g in 1:length(group_names)) {
        object@cache$variance_explained$r2_total[[g]][[m]] <- 100 * object@cache$variance_explained$r2_total[[g]][[m]]
        object@cache$variance_explained$r2_per_factor[[g]][,m] <- 100 * object@cache$variance_explained$r2_per_factor[[g]][,m]
      }
    }
  }
  
  ##############################
  ## Specify dimensionalities ##
  ##############################
  
  # Specify dimensionality of the data
  object@dimensions[["M"]] <- length(data)                            # number of views
  object@dimensions[["G"]] <- length(data[[1]])                       # number of groups
  object@dimensions[["N"]] <- sapply(data[[1]], ncol)                 # number of samples (per group)
  object@dimensions[["D"]] <- sapply(data, function(e) nrow(e[[1]]))  # number of features (per view)
  object@dimensions[["C"]] <- nrow(covariates[[1]])                        # number of covariates
  object@dimensions[["K"]] <- ncol(object@expectations$Z[[1]])        # number of factors
  
  # Assign sample and feature names (slow for large matrices)
  if (verbose) message("Assigning names to the different dimensions...")

  # Create default features names if they are null
  if (is.null(feature_names)) {
    print("Features names not found, generating default: feature1_view1, ..., featureD_viewM")
    feature_names <- lapply(seq_len(object@dimensions[["M"]]),
                            function(m) sprintf("feature%d_view_&d", as.character(seq_len(object@dimensions[["D"]][m])), m))
  } else {
    # Check duplicated features names
    all_names <- unname(unlist(feature_names))
    duplicated_names <- unique(all_names[duplicated(all_names)])
    if (length(duplicated_names)>0) 
      warning("There are duplicated features names across different views. We will add the suffix *_view* only for those features 
            Example: if you have both TP53 in mRNA and mutation data it will be renamed to TP53_mRNA, TP53_mutation")
    for (m in names(feature_names)) {
      tmp <- which(feature_names[[m]] %in% duplicated_names)
      if (length(tmp)>0) feature_names[[m]][tmp] <- paste(feature_names[[m]][tmp], m, sep="_")
    }
  }
  features_names(object) <- feature_names
  
  # Create default samples names if they are null
  if (is.null(sample_names)) {
    print("Samples names not found, generating default: sample1, ..., sampleN")
    sample_names <- lapply(object@dimensions[["N"]], function(n) paste0("sample", as.character(seq_len(n))))
  }
  samples_names(object) <- sample_names

  # Add covariates names
  # if(!is.null(object@covariates)){
  #   # Create default covariates names if they are null
  #   if (is.null(covariate_names)) {
  #     print("Covariate names not found, generating default: covariate1, ..., covariateC")
  #     covariate_names <- paste0("sample", as.character(seq_len(object@dimensions[["C"]])))
  #   }
  #   covariates_names(object) <- covariate_names
  # }
  
  # Set views names
  if (is.null(names(object@data))) {
    print("Views names not found, generating default: view1, ..., viewM")
    view_names <- paste0("view", as.character(seq_len(object@dimensions[["M"]])))
  }
  views_names(object) <- view_names
  
  # Set groups names
  if (is.null(names(object@data[[1]]))) {
    print("Groups names not found, generating default: group1, ..., groupG")
    group_names <- paste0("group", as.character(seq_len(object@dimensions[["G"]])))
  }
  groups_names(object) <- group_names
  
  # Set factors names
  factors_names(object)  <- paste0("Factor", as.character(seq_len(object@dimensions[["K"]])))
  
  ###################
  ## Parse factors ##
  ###################
  
  # Calculate variance explained estimates per factor
  if (is.null(object@cache[["variance_explained"]])) {
    object@cache[["variance_explained"]] <- calculate_variance_explained(object)
  } 
  
  # Remove inactive factors
  if (remove_inactive_factors) {
    r2 <- rowSums(do.call('cbind', lapply(object@cache[["variance_explained"]]$r2_per_factor, rowSums, na.rm=TRUE)))
    var.threshold <- 0.0001
    if (all(r2 < var.threshold)) {
      warning(sprintf("All %s factors were found to explain little or no variance so remove_inactive_factors option has been disabled.", length(r2)))
    } else if (any(r2 < var.threshold)) {
      object <- subset_factors(object, which(r2>=var.threshold), recalculate_variance_explained=FALSE)
      message(sprintf("%s factors were found to explain no variance and they were removed for downstream analysis. You can disable this option by setting load_model(..., remove_inactive_factors = FALSE)", sum(r2 < var.threshold)))
    }
  }
  
  # [Done in mofapy2] Sort factors by total variance explained
  if (sort_factors && object@dimensions$K>1) {

    # Sanity checks
    if (verbose) message("Re-ordering factors by their variance explained...")

    # Calculate variance explained per factor across all views
    r2 <- rowSums(sapply(object@cache[["variance_explained"]]$r2_per_factor, function(e) rowSums(e, na.rm = TRUE)))
    order_factors <- c(names(r2)[order(r2, decreasing = TRUE)])

    # re-order factors
    object <- subset_factors(object, order_factors)
  }

  # Mask outliers
  if (remove_outliers) {
    if (verbose) message("Removing outliers...")
    object <- .detect_outliers(object)
  }
  
  # Mask intercepts for non-Gaussian data
  if (any(object@model_options$likelihoods!="gaussian")) {
    for (m in names(which(object@model_options$likelihoods!="gaussian"))) {
      for (g in names(object@intercepts[[m]])) {
        object@intercepts[[m]][[g]] <- NA
      }
    }
  }

  # ######################
  # ## Quality controls ##
  # ######################
  # 
  # if (verbose) message("Doing quality control...")
  # object <- .quality_control(object, verbose = verbose)
  # 
  return(object)
}

mofa_trained <- load_model(outfile)
samples_names(mofa_trained) <- samples_names(mofa)
rownames(samples_metadata(mofa_trained)) <- samples_metadata(mofa_trained)[["sample"]]
```

### Visualize variance explained by factors

```{r}
plot_variance_explained(mofa_trained, x='factor', y='group', split_by = 'view', plot_total = TRUE, max_r2 = 50)[[1]] +
  theme(axis.text.x = element_text(angle=45, hjust=1))

get_variance_explained(mofa_trained, as.data.frame = TRUE)[[2]] %>%
  ggplot(aes(group, value)) +
  geom_col() +
  coord_flip() +
  ylab("Var. (%)") +
  theme_classic(base_size=14)
```

Plot by celltype
```{r, fig.width=10, fig.height=10}
get_variance_explained(mofa_trained, as.data.frame = TRUE)[[1]] %>%
  ggplot(aes(factor, value)) + geom_col() +
  coord_flip() +
  facet_wrap(group~., ncol = 6, scales = "free_x")
```

```{r}
plot_factor_cor(mofa_trained, method = "spearman")
```


```{r}
## Correlation with principal components
pcs <- reducedDim(sce)
fctrs <- get_factors(mofa_trained) %>%
  purrr::reduce(rbind)

corrplot::corrplot(cor(pcs, fctrs[rownames(pcs),]))
```

#### Factor ID plots

```{r, fig.height=10, fig.width=10}
plot_factor_ordered <- function(mofa_trained, f){
  factor_df <- get_factors(mofa_trained, factors = f, as.data.frame = TRUE) %>%
      mutate(organ = sapply(str_split(sample, "_"), function(x) x[2])) %>%
      group_by(group) %>%
      mutate(gr_mean = median(value)) %>%
      ungroup() %>%
      arrange(gr_mean) %>%
      mutate(group=factor(group, levels=unique(group))) 
  
  r2_df <- get_variance_explained(mofa_trained, factors = f, as.data.frame = TRUE)[[1]] %>%
    filter(factor==paste0('Factor',f)) %>%
    mutate(group=factor(group, levels = levels(factor_df$group)))
  
  pl1 <- factor_df %>%
      ggplot(aes(group, value)) +
      geom_boxplot() +
      geom_jitter(aes(color= organ), size=0.7) +
      geom_hline(yintercept = 0, linetype=2) +
      coord_flip() +
      ylab(paste0("Factor ", f)) +
      theme_bw(base_size = 14)
  
  pl2 <- r2_df %>%
    ggplot(aes(group, value)) +
    geom_col() +
    coord_flip() +
    ylab("% variance explained") +
    theme_bw(base_size = 14) +
    remove_y_axis()
  
  pl1 + pl2 + plot_layout(widths=c(2,1), guides="collect") 
}

get_top_celltype_per_factor <- function(mofa_trained, f){
  r2_df <- get_variance_explained(mofa_trained, factors = f, as.data.frame = TRUE)[[1]] %>%
    filter(factor==paste0('Factor',f)) 
    # mutate(group=factor(group, levels = ))
  top_quant_r2 <- quantile(r2_df$value, probs = seq(0, 1, by = 0.2))["80%"]
  top_groups <- r2_df$group[r2_df$value >= top_quant_r2]
  return(top_groups)
}

save_factor_id <- function(mofa_trained, f, figdir){
  ## Order celltypes by factor values
  p1 <- plot_factor_ordered(mofa_trained, f)
  
  ## Plot factor values across organs for celltypes with high variance explained
  p2 <- plot_factor(mofa_trained, factors = f, groups = get_top_celltype_per_factor(mofa_trained, f), group_by = "group", 
              color_by = "organ", 
              dot_size = 2, dodge = TRUE
              )
  
  ## Plot factor weights on genes
  # plot_data_heatmap(mofa_trained, factor = f, nfeatures = 50, text_size = 3, show_colnames=FALSE,
  #                   annotation_samples = c("organ", "time", "method", "donor"))
  p3 <- plot_weights(mofa_trained, factors = f, nfeatures = 30, text_size = 3) +
   scale_y_discrete(expand=c(0.1, 0.1))
  
  (p1 | (p2 / p3)) +
    plot_layout(guides="collect") +
    ggsave(glue("{figdir}/MOFA_{split}_factorID_factor{f}.pdf"), width = 15, height = 10)
}

for (f in 1:mofa_trained@dimensions$K){
  print(paste0("Saving ID for Factor ", f, "..."))
  save_factor_id(mofa_trained, f=f, figdir = figdir)  
}

# save_factor_id(mofa_trained, f=1, figdir = figdir)  
# plot_weights(mofa_trained, factors = f, nfeatures = 30, text_size = 3) +
#    scale_y_discrete(expand=c(0.1, 0.1))
```


<!-- ```{r} -->
<!-- get_top_celltype_per_factor <- function(mofa_trained, f){ -->
<!--   r2_df <- get_variance_explained(mofa_trained, factors = f, as.data.frame = TRUE)[[1]] %>% -->
<!--     filter(factor==paste0('Factor',f)) %>% -->
<!--     mutate(group=factor(group, levels = levels(factor_df$group))) -->
<!--   top_quant_r2 <- quantile(r2_df$value, probs = seq(0, 1, by = 0.2))["80%"] -->
<!--   top_groups <- r2_df$group[r2_df$value >= top_quant_r2] -->
<!--   return(top_groups) -->
<!-- } -->

<!-- plot_factor(mofa_trained, factor=2, groups=get_top_celltype_per_factor(mofa_trained, 2)[3:5], dodge = TRUE, add_boxplot = TRUE, color_by="donor") -->
<!-- plot_factor(mofa_trained, factor=2, groups=get_top_celltype_per_factor(mofa_trained, 2)[3:5], group_by = "organ", dodge = TRUE, add_boxplot = TRUE, color_by="organ") + -->
<!--   ylim(3,8) -->
<!-- ``` -->
<!-- ```{r} -->
<!-- get_factors(mofa_trained, groups=get_top_celltype_per_factor(mofa_trained, 2)[3:5], factor=2, as.data.frame = TRUE) %>% -->
<!--   left_join(mofa_trained@samples_metadata) %>% -->
<!--   ggplot(aes(value, fill=organ)) + -->
<!--   geom_histogram() -->
<!--   # geom_smooth(method="lm") + -->
<!--   ggpubr::stat_cor() -->
<!-- plot_factors_vs_cov(mofa_trained, groups=get_top_celltype_per_factor(mofa_trained, 2)[3:5], covariates = "") -->
<!-- ``` -->
<!-- ```{r} -->
<!-- w <- get_weights(mofa_trained, factors = 'all', as.data.frame = FALSE) -->
<!-- as.data.frame(w$scaled_logcounts) %>% -->
<!--   rownames_to_column("gene") %>% -->
<!--   write_csv("~/MOFA_weights.csv") -->
<!-- ``` -->

#### KNN graph per celltype

```{r}
## Get factors that explain most variance in each celltype
get_top_factor_per_celltype <- function(mofa_trained, gr, min_R2=2){
  get_variance_explained(mofa_trained, as.data.frame = TRUE)[[1]] %>%
    filter(group==gr) %>%
    filter(value >= min_R2) %>%
    pull(factor) %>%
    as.character()
}

## Make KNN graph based on similarity of top factors for each celltype
get_ct_KNN_graph <- function(mofa_trained, gr, min_R2=5, k=5){
  ## Get factors that explain most variance per celltype
  fs <- get_top_factor_per_celltype(mofa_trained, gr, min_R2 = min_R2)
  
  ## Make KNN graph from top factors
  Z <- get_factors(mofa_trained, groups=gr, factors = fs)[[1]]
  knn_ct <- buildKNNGraph(t(Z), k=k)
  
  ## Add attributes
  metadata_ct <- samples_metadata(mofa_trained)[rownames(Z),]
  # covariates
  V(knn_ct)$organ <- metadata_ct$organ
  V(knn_ct)$age <- metadata_ct$age
  V(knn_ct)$n_cells <- metadata_ct$n_cells
  V(knn_ct)$method <- metadata_ct$method
  V(knn_ct)$donor <- metadata_ct$donor
  # top factors
  for (c in colnames(Z)){
   vertex_attr(knn_ct)[[c]] <- Z[,c]  
  }
  
  return(knn_ct)
  }

## Plot KNN graph
plot_ct_KNN_graph <- function(knn, color_by="organ"){
  ## Define color 
  if (!color_by %in% names(vertex_attr(knn))){
    stop("specified color_by variable is not in vertex_attr(knn)")
  }
  
  if (color_by=="organ"){ 
    scale_color_knngraph <- scale_color_manual(values=org_colors)
  } else if (is.numeric(vertex_attr(knn, color_by))){
    scale_color_knngraph <- scale_color_viridis_c(option="magma")  
  } else {
      scale_color_knngraph <- scale_color_discrete()
    }
  
  vertex_attr(knn, "color_by") <- vertex_attr(knn, color_by)
  
  ggraph(knn) +
    geom_edge_link0() +
    geom_node_point(aes(color=color_by, size=n_cells)) +
    theme(panel.background = element_blank()) +
    scale_color_knngraph +
    scale_size(range=c(2,7)) 
  }

get_top_factor_per_celltype(mofa_trained, "CD8+T", min_R2 = 5)
plot_ct_KNN_graph(get_ct_KNN_graph(mofa_trained, "SMALL PRE B CELL", k=5), color_by = 'organ') +
  plot_ct_KNN_graph(get_ct_KNN_graph(mofa_trained, "SMALL PRE B CELL", k=5), color_by = 'Factor4')

all_groups <- names(get_data(mofa_trained)[[1]])
knn_graph_pl <- lapply(all_groups, function(g){
  knn <- get_ct_KNN_graph(mofa_trained, g, k=5, min_R2 = 2)
  plot_ct_KNN_graph(knn, color_by = 'organ') + ggtitle(g)
  })

knn_graph_pl <- setNames(knn_graph_pl, all_groups)
knn_graph_pl$TREG
```

```{r}
## Score connectivity between samples from the same organ
.calc_connectivity_score <- function(knn, o){
  adj <- get.adjacency(knn)
  n_org <- sum(V(knn)$organ==o)
  n_other <- sum(V(knn)$organ!=o)
  within_edges <- sum(adj[V(knn)$organ==o,V(knn)$organ==o])
  between_edges <- sum(adj[V(knn)$organ==o,V(knn)$organ!=o])
  score <- (within_edges/between_edges)*(n_other/n_org)
  return(score)
  }

## Calculate connectivity score for permutations of node labels
conn_score_test <- function(knn, o, n_perm=1000){
  real_score <- .calc_connectivity_score(knn, o)
  ## Random permutations
  rand_scores <- c()
  for (i in 1:n_perm){
    rand_knn <- knn
    V(rand_knn)$organ <- sample(V(knn)$organ)
    rand_scores <- c(rand_scores, .calc_connectivity_score(rand_knn, o))   
  }
  
  p_val <- sum(c(rand_scores, real_score) >= real_score)/(n_perm + 1)
  if (p_val < 2e-16){ p_val <- 2e-16}
  return(c('score'=real_score,'p_value'=p_val))
}

## Calculate connectivity score + significance with permutation test
test_conn_group <- function(mofa_trained, g, k=5, min_R2 = 2, n_perm=1000){
  knn <- get_ct_KNN_graph(mofa_trained, g, k=k, min_R2 = min_R2)
  test_orgs <- names(table(V(knn)$organ))[table(V(knn)$organ) > 2]
  return(sapply(test_orgs, function(o) conn_score_test(knn, o, n_perm=n_perm)))
  }

connectivity_test_ls <- lapply(all_groups, function(g) test_conn_group(mofa_trained, g))
connectivity_test_ls <- setNames(connectivity_test_ls, all_groups)

connectivity_test_df <- imap(connectivity_test_ls, ~ data.frame(t(.x)) %>% rownames_to_column("organ") %>% mutate(group=.y)) %>%
  purrr::reduce(bind_rows) %>%
  mutate(is_signif = ifelse(p_value < 0.01, TRUE, FALSE)) 

connectivity_test_df %>%
  ggplot(aes(organ, group,fill=log10(score))) +
  geom_tile() +
  scale_fill_distiller(palette="Reds", direction = 1) +
  geom_text(data=. %>% filter(is_signif), label="*", size=5)

```
```{r, fig.height=10, fig.width=10}
connectivity_test_df %>%
  group_by(group) %>%
  mutate(mean_val=median(score)) %>%
  ungroup() %>%
  arrange(-mean_val) %>%
  mutate(group=factor(group, levels=unique(group))) %>%
  ggplot(aes(organ, log1p(score))) +
  geom_col(fill="grey") +
  geom_col(data=. %>% filter(is_signif), aes(fill=organ)) +
  scale_fill_manual(values=org_colors)  +
  coord_flip() +
  facet_grid(group~.) +
  theme(strip.text.y = element_text(angle=0))
```

#### Expression of top R2 factors

```{r}
get_top_weight_genes <- function(mofa_trained, f, n_top=20, which="top"){
  w_df <- get_weights(mofa_trained, factors = f, as.data.frame = TRUE) %>%
    arrange(value) 
  if (which=="top") {
    w_df %>%
      top_n(n_top, value) %>%
      pull(feature) %>%
      as.character()
  } else if (which=="bottom"){
    w_df %>%
      top_n(n_top, -value) %>%
      pull(feature) %>%
      as.character()
    }
}

plot_data_top_weights <- function(mofa_trained, ct, f, n_top=20, which="top"){
  genes <- get_top_weight_genes(mofa_trained, f, which=which, n_top=n_top)
  data <- get_data(mofa_trained, groups=ct)[[1]][[1]][genes,]
  
  pl_df <- reshape2::melt(data, varnames=c("gene", "sample")) %>%
    left_join(samples_metadata(mofa_trained)) %>%
    arrange(age) %>%
    mutate(sample=factor(sample, levels=unique(sample))) %>%
    group_by(gene) %>%
    mutate(value=scale(value))
  pl_df %>%
    ggplot(aes(sample, gene, fill=value)) +
    geom_tile() +
    facet_grid(.~organ, space="free", scales="free") +
    scale_fill_gradient2(high="red", low="blue", name="Scaled\nexpression") +
    xlab("----age--->") + ylab(glue("{which} weight genes")) +
    theme_bw(base_size=16) +
    theme(axis.ticks.x = element_blank(), axis.text.x = element_blank()) +
    ggtitle(glue('{ct} - {f}'))
}

for (g in all_groups){
  fs <- get_top_factor_per_celltype(mofa_trained, g, min_R2=3)
  top_plots <- lapply(fs, function(x) (plot_data_top_weights(mofa_trained, g, x, which="top") + remove_x_axis()) /  
                        plot_data_top_weights(mofa_trained, g, x, which="bottom") + ggtitle("")
  )
  wrap_plots(top_plots, ncol=1) +
  ggsave(glue("{figdir}/top_factors_expr_{g}.pdf"), width=8, height = 7*length(top_plots))
}

```
```{r, fig.width=18}
plot_data_heatmap(mofa_trained, factor = 2, show_colnames=FALSE, annotation_samples = c("anno_lvl_2_final_clean", "organ"))
plot_factor(mofa_trained, factor=25, color_by="method", dot_size = 4)
```

### GSEA
```{r}
# BiocManager::install("MOFAdata")
library(MOFAdata)
utils::data(reactomeGS)
head(rownames(reactomeGS))

## Remove row with NA
reactomeGS <- reactomeGS[!is.na(rownames(reactomeGS)),]
```

```{r}
library(EnsDb.Hsapiens.v86)
hg.pairs <- readRDS(system.file("exdata", "human_cycle_markers.rds", package="scran"))
all_genes <- ensembldb::genes(EnsDb.Hsapiens.v86)
detach(package:EnsDb.Hsapiens.v86)
detach(package:ensembldb)

# gene_name_2_id <- function(gene){
#    return(all_genes[all_genes$gene_name==gene,]$gene_id[1])
# }
# 
# gene_ids <- sapply(mofa_trained@features_metadata$feature, gene_name_2_id)
# rowData(sce)["gene_id"] <- gene_ids
# rowData(sce)["gene_name"] <- rownames(sce)

gene_names_reactome <- all_genes[colnames(reactomeGS)]$gene_name
colnames(reactomeGS) <- gene_names_reactome
```

Subset to genes tested
```{r}
reactomeGS_universe <- reactomeGS[, colnames(reactomeGS) %in% mofa_trained@features_metadata$feature]
```


```{r, fig.width=15, fig.height=7}
# GSEA on positive weights, with default options
res.positive <- run_enrichment(mofa_trained,
  view='scaled_logcounts',
  # statistical.test = 'cor.adj.parametric',
  feature.sets = reactomeGS_universe, 
  sign = "positive",
)

# GSEA on negative weights, with default options
res.negative <- run_enrichment(mofa_trained, 
  view='scaled_logcounts',
  # statistical.test = 'cor.adj.parametric',
  feature.sets = reactomeGS_universe, 
  sign = "negative"
)


for (f in 1:mofa_trained@dimensions$K){
  if (min(res.positive$pval.adj[,paste0("Factor", f)]) < 0.1) {
    print(plot_enrichment(res.positive, factor = f, alpha=0.1) + ggtitle("Positive weights") +
            plot_enrichment(res.negative, factor = f, alpha=0.1) + ggtitle("Negative weights") +
              plot_annotation(title=paste0("Factor", f)))
      }
  }
```

```{r}
signif_pathways <- rownames(data.frame(res.negative$pval.adj))[order(data.frame(res.negative$pval.adj)[["Factor8"]])[0:10]]
colnames(reactomeGS_universe)[reactomeGS_universe[signif_pathways[5],]==1]
plot_enrichment_detailed(res.negative, factor = 8)
```

<!-- ### Factor annotation  -->
<!-- So far -->

<!-- #### Factor 1 -->
<!-- Cell cycle / proliferation signature -->

<!-- #### Factor 2 -->
<!-- Explains variation in late B cell stages, possibly difference between BM and other organs? -->

<!-- #### Factor 3 -->
<!-- Thymus specific T cell signature, especially in immature T cells. Interestingly, difference also in B1 cells, could be signalling from thymic microenvironment? -->

<!-- #### Factor 4 -->
<!-- Variation within progenitors, and lots of variance explained in B1 cells too! Stemness markers such as CD34, HOPX... Explains lots of variance in Tregs (7.67%) -->

<!-- #### Factor 5 -->
<!-- ILC specific -->

<!-- #### Factor 7 -->
<!-- Could be signature of spleen specific progenitors, or spleen soup -->

<!-- #### Factor 8 -->
<!-- More cell cycle/proliferation, but lower in thymus samples, TH samples express proteasome -->

<!-- #### Factor 10 -->
<!-- mature VS pro B cells -->

<!-- ```{r} -->
<!-- get_factors(mofa_trained, factors = 7, as.data.frame = TRUE) %>% -->
<!--   left_join(mofa_trained@samples_metadata) %>% -->
<!--   ggplot(aes(value, fill=organ)) + -->
<!--   geom_histogram() -->
<!-- ``` -->
<!-- ```{r} -->
<!-- library(pROC) -->

<!-- get_organ_auc <- function(mofa_trained, f, o, groups){ -->
<!--     df <- get_factors(mofa_trained, factors = f, as.data.frame = TRUE, groups = groups) %>% -->
<!--     left_join(mofa_trained@samples_metadata) -->

<!--   cat <- as.numeric(df$organ==o) -->
<!--   pred <- df$value -->
<!--   if (sum(cat) > 0) { -->
<!--     roc_obj <- roc(cat, pred) -->
<!--     auc <- auc(roc_obj) -->
<!--     return(as.vector(auc)) -->
<!--     } -->
<!-- } -->

<!-- top_gr_df <- lapply(1:19, function(f) data.frame(top_group=get_top_celltype_per_factor(mofa_trained, f), factor=f)) %>% -->
<!--   purrr::reduce(bind_rows)  -->

<!-- org = "BM" -->
<!-- AUC_org <- sapply(1:nrow(top_gr_df), function(i){ -->
<!--   get_organ_auc(mofa_trained,  -->
<!--                 o=org, -->
<!--                 f=top_gr_df$factor[i],  -->
<!--                 groups = top_gr_df$top_group[i])} -->
<!--   ) -->
<!-- AUC_org[sapply(AUC_org, is.null)] <- NA -->
<!-- top_gr_df[["AUC_org"]] <- unlist(AUC_org) -->

<!-- ggplot(top_gr_df, aes(factor, fill=AUC_org, top_group))  + -->
<!--   geom_tile() + -->
<!--   geom_text(aes(label=round(AUC_org, 2))) + -->
<!--   scale_fill_viridis_c() -->

<!-- ``` -->

<!-- --- -->

<!-- ```{r} -->
<!-- library(EnsDb.Hsapiens.v86) -->
<!-- hg.pairs <- readRDS(system.file("exdata", "human_cycle_markers.rds", package="scran")) -->
<!-- all_genes <- ensembldb::genes(EnsDb.Hsapiens.v86) -->

<!-- gene_name_2_id <- function(gene){ -->
<!--    return(all_genes[all_genes$gene_name==gene,]$gene_id[1]) -->
<!-- } -->

<!-- gene_ids <- sapply(rownames(sce), gene_name_2_id) -->
<!-- rowData(sce)["gene_id"] <- gene_ids -->
<!-- rowData(sce)["gene_name"] <- rownames(sce) -->

<!-- rownames(sce) <- rowData(sce)[["gene_id"]] -->

<!-- assignments <- cyclone(sce, hg.pairs, assay.type="logcounts") -->

<!-- ## Add "phase" assignments to mofa -->
<!-- sce$cellcycle_phase <- assignments$phases -->
<!-- samples_metadata(mofa_trained)  <- samples_metadata(mofa_trained) %>% -->
<!--   mutate(cellcycle_phase=sce[,match(samples_metadata(mofa_trained)$sample, colnames(sce))]$cellcycle_phase) -->
<!-- ``` -->

<!-- ```{r} -->
<!-- plot_factors(mofa_trained, factors = 1, color_by = "cellcycle_phase") -->
<!-- ``` -->


<!-- <!-- ```{r, fig.width=15, fig.height=5} --> -->
<!-- <!-- get_factors(mofa_trained, factors = 3, as.data.frame = TRUE) %>% --> -->
<!-- <!--   mutate(organ = sapply(str_split(sample, "-"), function(x) x[length(x)-3])) %>% --> -->
<!-- <!--   group_by(group) %>% --> -->
<!-- <!--   mutate(gr_mean = median(value)) %>% --> -->
<!-- <!--   ungroup() %>% --> -->
<!-- <!--   arrange(gr_mean) %>% --> -->
<!-- <!--   mutate(group=factor(group, levels=unique(group))) %>% --> -->
<!-- <!--   ggplot(aes(organ, value, color=organ)) + --> -->
<!-- <!--   geom_boxplot() + --> -->
<!-- <!--   geom_jitter() + --> -->
<!-- <!--   # geom_hline(yintercept = 0, linetype=2) + --> -->
<!-- <!--   coord_flip() + --> -->
<!-- <!--   facet_wrap(.~group, scales = "free_x") --> -->
<!-- <!--             group_by = "group",  dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE) + --> -->
<!-- <!--   coord_flip() --> -->
<!-- <!-- ``` --> -->


<!-- ## Go by celltype instead of factor -->

<!-- ### DC1 -->
<!-- ```{r} -->
<!-- get_variance_explained(mofa_trained, as.data.frame = TRUE)[[1]] %>% -->
<!--   filter(group=="DC1") %>% -->
<!--   ggplot(aes(factor, value)) + geom_col() + -->
<!--   coord_flip() + -->
<!--   facet_wrap(group~., ncol = 6, scales = "free_x") -->
<!-- ``` -->
<!-- ```{r} -->
<!-- plot_factors(mofa_trained, factors = c(2,4), color_by = "organ", groups = "DC1") -->
<!-- ``` -->

<!-- ```{r, fig.width=12, fig.height=4} -->
<!-- plot_factor(mofa_trained, factors = c(4), color_by = "organ", group_by = "organ", groups = "DC1") -->
<!-- plot_factor(mofa_trained, factors = 4, group_by = "group", color_by = "organ", dot_size = 0.8, add_boxplot = TRUE, dodge = TRUE) -->
<!-- ``` -->
<!-- ```{r} -->
<!-- plot_weights(mofa_trained, factors = 4, nfeatures = 30) -->
<!-- ``` -->
<!-- ```{r} -->
<!-- plot_data_scatter(mofa_trained, factor = 4, groups="DC1", color="organ", features="HLA-DRA") -->
<!-- ``` -->

<!-- ## Explore by factor -->
<!-- ```{r} -->
<!-- plot_factor(mofa_trained, factor = 3) -->
<!-- plot_weights(mofa_trained, factor = 3, nfeatures = 20) -->
<!-- ``` -->


<!-- ## Find factors that discriminate between organs -->


<!-- ```{r} -->
<!-- get_organ_AUC <- function(mofa_trained, f, gr){ -->
<!--   f_df <- get_factors(mofa_trained, factors = f, groups = gr, as.data.frame = TRUE) %>% -->
<!--     # group_by(group) %>% -->
<!--     # mutate(value=scale(value)) %>% -->
<!--     # ungroup() %>% -->
<!--     mutate(organ = sapply(str_split(sample, "-"), function(x) x[length(x)-3]))  -->
<!--   organs <- unique(f_df$organ) -->
<!--   suppressWarnings(suppressMessages({org_auc <- sapply(organs, function(org) roc(as.numeric(f_df$organ==org), f_df$value)$auc)})) -->
<!--   all_organs <- as.character(unique(mofa_trained@samples_metadata$organ)) -->
<!--   org_auc <- setNames(org_auc[all_organs], all_organs) -->
<!--   return(org_auc) -->
<!-- } -->

<!-- all_organs <- as.character(unique(mofa_trained@samples_metadata$organ)) -->
<!-- all_groups <- as.character(unique(mofa_trained@samples_metadata$group)) -->

<!-- ## Mask if too little samples -->
<!-- n_samples_mat <- samples_metadata(mofa_trained) %>% -->
<!--   group_by(organ, group) %>% -->
<!--   summarise(n_samples=n()) %>% -->
<!--   pivot_wider(id_cols=c(group), names_from="organ", values_from="n_samples", values_fill=0) %>% -->
<!--   column_to_rownames("group") %>% -->
<!--   as.matrix() -->

<!-- mask_pairs <- t(n_samples_mat < 3) -->

<!-- AUC_mat <- sapply(all_groups, function(g) get_organ_AUC(mofa_trained, f=10, gr=g)) -->
<!-- AUC_mat[mask_pairs[rownames(AUC_mat), colnames(AUC_mat)]] <- NA -->

<!-- AUC_thresh = 0.8 -->
<!-- reshape2::melt(AUC_mat, varnames=c("organ", "group"), value.name="AUC") %>% -->
<!--   ggplot(aes(organ, group)) + -->
<!--   geom_point(aes(size=AUC, color=AUC)) + -->
<!--   geom_point(data=. %>% filter(AUC > AUC_thresh), shape=8, size=2,color="white") + -->
<!--   scale_size(limits = c(0.5,1)) + -->
<!--   scale_color_gradientn(colours = RColorBrewer::brewer.pal(5, "Reds")) -->
<!-- ``` -->


<!-- ```{r, fig.width=15, fig.height=4} -->
<!-- library(patchwork) -->
<!-- plot_factor(mofa_trained, factors = 5, group_by = "group", color_by = "organ", dodge = TRUE, add_boxplot = TRUE)  -->

<!--   plot_layout(guides="collect") -->

<!-- ``` -->
<!-- ```{r} -->
<!-- plot_weights(mofa_trained, factors = 5, nfeatures = 30) -->
<!-- ``` -->
<!-- ```{r} -->
<!-- plot_data_heatmap(mofa_trained, factor = 5, show_colnames=FALSE) -->
<!-- ``` -->



<!-- # Model 3 -  MEFISTO  -->

<!-- Add time as covariate to run MEFISTO -->

<!-- ```{r} -->
<!-- ## Vector for time assignment -->
<!-- times <- distinct(data.frame(age=sce$age, new_sample)) %>% -->
<!--   column_to_rownames('new_sample') %>% -->
<!--   .[sample_names_unique,] -->

<!-- samples_metadata(mofa)[["time"]] <- times -->

<!-- mofa <- set_covariates(mofa, covariates = "time") -->
<!-- mofa -->
<!-- ``` -->
<!-- ```{r, fig.height=15, fig.width=10} -->
<!-- gg_input <- plot_data_overview(mofa, -->
<!--                                show_covariate = TRUE, -->
<!--                                show_dimensions = TRUE)  -->
<!-- gg_input -->
<!-- ``` -->

<!-- <!-- Keep groups that span multiple views --> -->
<!-- <!-- ```{r} --> -->
<!-- <!-- gr_samples <- split(samples_metadata(mofa)$sample, samples_metadata(mofa)$group) --> -->
<!-- <!-- all(is.na(data$BM[,gr_samples$Basophil])) --> -->
<!-- <!-- lapply(unique(samples_metadata(mofa)[["group"]]), function(x) data$BM[]) --> -->


<!-- <!-- mofa@data --> -->
<!-- <!-- subse(mofa)[,samples_metadata(mofa)[["group"]] == "Basophil"] --> -->
<!-- <!-- ``` --> -->

<!-- Prepare 4 training -->

<!-- ```{r} -->
<!-- data_opts <- get_default_data_options(mofa) -->

<!-- model_opts <- get_default_model_options(mofa) -->
<!-- model_opts$num_factors <- 10 -->

<!-- train_opts <- get_default_training_options(mofa) -->
<!-- train_opts$seed <- 2020 -->
<!-- train_opts$convergence_mode <- "fast" # use "fast" for faster training -->

<!-- mefisto_opts <- get_default_mefisto_options(mofa) -->
<!-- mefisto_opts$warping <- FALSE -->
<!-- # mefisto_opts$sparseGP <- TRUE -->

<!-- mofa <- prepare_mofa( -->
<!--   object = mofa, -->
<!--   data_options = data_opts, -->
<!--   model_options = model_opts, -->
<!--   training_options = train_opts, -->
<!--   mefisto_options = mefisto_opts -->
<!-- )  -->
<!-- ``` -->

<!-- ## Train -->

<!-- ```{r} -->
<!-- outfile <- "/nfs/team205/ed6/data/Fetal_immune/myeloid_mefisto_model.hdf5" -->
<!-- mofa_trained <- run_mofa(mofa, outfile = outfile) -->
<!-- ``` -->

<!-- ## Load trained model -->
<!-- ```{r} -->
<!-- mofa_trained <- load_model(outfile, load_interpol_Z = TRUE) -->
<!-- ``` -->

